Best-corrected visual acuity (BCVA) is a key metric for evaluating visual function and treatment outcomes in ophthalmology. While traditional tools like the Snellen chart are widely used, they have limitations in accuracy and standardization. LogMAR charts and contrast sensitivity tests offer improved precision but still depend on subjective patient responses. Recent advances in deep learning, particularly convolutional neural networks (CNNs), have enabled more objective and accurate BCVA estimation from fundus images. Although promising, this application remains underexplored and holds potential for enhancing future ophthalmic diagnostics. Additionally, fundus biomicroscopy and binocular indirect ophthalmoscopy (BIO) are essential, noninvasive techniques for examining the posterior segment of the eye. These stereoscopic methods provide a wide and detailed view of the retina and vitreous, even in cases of media opacity. BIO uses a condensing lens and a head-mounted device with mirrors and light to produce an inverted image, enabling dynamic observation of peripheral retina when combined with scleral depression. Lens power influences magnification and field of view, with specific lenses adapted for adult and pediatric exams. Pharmacologic pupil dilation is often required for optimal visualization. BIO remains fundamental in diagnosing peripheral retinal pathologies, such as retinal tears and diseases like retinopathy of prematurity and Coats’ disease.